Classifying Accurate Heart Rate Measurements From Smartwatches

With the wide-distribution of smart wearables, it seems as
though ubiquitous healthcare can finally permeate into our
everyday lives, opening the possibility to realize clinical-grade applications. However, given that clinical applications
require reliable sensing, there is a need to understand how
accurate healthcare sensors on wearable devices (e.g., heart
rate sensors) are. To answer this question, this work starts
with a thorough investigation on the accuracy of widely used
wearable devices’ heart rate sensors. Specifically, we show
that when actively moving, heart rate readings can diverge
far from the ground truth, and also show that such inaccuracies cannot be easily correlated, nor predicted, using accelerometer and gyroscope measurements. Rather, we point
out that the light intensity readings at the photoplethysmography (PPG) sensor can be an effective indicator of
heart rate accuracy. Using a Viterbi algorithm-based Hidden Markov Model, we show that it is possible to design a
filter that allows smartwatches to self-classify measurement
quality with ~98% accuracy.

Project Members:

Jungmo Ahn

JeongGil Ko

Ho-Kyeong Ra (DGIST)

Hee Jung Yoon (DGIST)

Sang Hyuk Son (DGIST)

Software:

All software components used in this project is available through our Git Repository.